Participation in preventive health interventions is largely motivated by perceptions of risks of the diseases and benefits of the interventions, and social networks can strongly influence perceptions. In this proposal we are interested in how risk/benefit perceptions are influenced by health-outcomes of those in one's social sphere and how these lead to individual-level decisions that change population-level outcomes. By developing and analyzing mathematical simulation models, we aim to develop a framework for studying the mutual interaction between preventive health behaviors, risk perception, and individual health outcomes on various social networks. We address our interest by focusing on two specific examples:(a) breast cancer screening and (b) seasonal influenza vaccination. These provide an example for a non-contagious and a contagious disease respectively. Our models will use both empirical social networks and constructed random, small-world and scale-free social networks with similar mean connectivities. We shall conduct surveys to gather individual-level behavioral data that will allow parameterization of the models. In contrast to past and standard approaches, our models include two important properties of human decision-making: (a) memory and adaptability from past experiences and (b) peer-influences via rumor/information spreading. Thus our individuals, represented as nodes in the network, are assumed to have bounded-rationality and are influenced by their acquaintances in their social sphere when making preventive health decisions. Our study will allows to understand how individual-level preventive health behaviors change population-level outcomes. Our model would then allow us to test and suggest different public health incentives and how these could lead to improved population-level health. PUBLIC HEALTH RELEVANCE: Participation in preventive health interventions is largely motivated by perceptions of risks of the diseases and benefits of the interventions, and social networks can strongly influence perceptions. In this proposal, we will combine mathematical simulation models and individual-level surveys to study how population-level health emerges and evolves in time through the collective action of individual behavior when individuals are influenced by health outcomes of others through their social network.